Mass and charge transport phenomena in porous transport layer for proton exchange membrane water electrolyzers: A review
Bibliographic record
Abstract
Proton exchange membrane (PEM) water electrolysis offers a promising method for green hydrogen production, utilizing renewable sources and generating oxygen without carbon emissions, making it highly energy-efficient theoretically, and environmentally friendly. However, the global hydrogen production from water electrolysis is limited to ∼4 % only, due to the high cost of hydrogen production (USD 3–7/ kg) arising from the lower efficiency of the electrolyzers operated especially at higher current densities. PEM water electrolysis efficiency loss is primarily associated with mass and charge transport resistances at the interface of the porous transport layer (PTL), catalyst layer, and bipolar plates. These losses increase the voltage requirements for the PEM water electrolysis and increase the cost of hydrogen produced. PTL is an important component of PEM water electrolyzer which facilitates uniform gas distribution, and efficient electrolyte management, provides mechanical support, and ensures efficient thermal management by dissipating excess heat. The efficiency of a PEM water electrolyzer is directly dependent on different transport processes occurring through the cross-section and on the surface of PTL. Hence improving and controlling the transport processes in PTL is of paramount importance. This review compiles the current knowledge describing the mass and charge transport losses due to anode PTL and overviews relevant characterization techniques to monitor and mitigate the efficiency losses for improved electrolyzer performance. • Investigate the role of PTL structure on water management inside the stack. • Analysis the role of bubble generation and elimination for efficient mass transport. • Elucidate the impact of ICR between the PTL and catalyst layers on cell performances. • Summarize in-situ and ex-situ characterization techniques for PTL transport study.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".